In vivo capture and label-free detection of early metastatic cells

Samira M. Azarin, Ji Yi, Robert M. Gower, Brian A. Aguado, Megan E. Sullivan, Ashley G. Goodman, Eric J. Jiang, Shreyas S. Rao, Yinying Ren, Susan L. Tucker, Vadim Backman (), Jacqueline S. Jeruss () and Lonnie D Shea ()
Additional contact information
Samira M. Azarin: University of Minnesota
Ji Yi: Northwestern University
Robert M. Gower: University of South Carolina
Brian A. Aguado: Northwestern University
Megan E. Sullivan: Northwestern University Feinberg School of Medicine
Ashley G. Goodman: Northwestern University
Eric J. Jiang: Northwestern University
Shreyas S. Rao: Northwestern University
Yinying Ren: Northwestern University
Susan L. Tucker: The University of Texas MD Anderson Cancer Center
Vadim Backman: Northwestern University
Jacqueline S. Jeruss: University of Michigan
Lonnie D Shea: Northwestern University

Nature Communications, 2015, vol. 6, issue 1, 1-9

Abstract: Abstract Breast cancer is a leading cause of death for women, with mortality resulting from metastasis. Metastases are often detected once tumour cells affect the function of solid organs, with a high disease burden limiting effective treatment. Here we report a method for the early detection of metastasis using an implanted scaffold to recruit and capture metastatic cells in vivo, which achieves high cell densities and reduces the tumour burden within solid organs 10-fold. Recruitment is associated with infiltration of immune cells, which include Gr1hiCD11b+ cells. We identify metastatic cells in the scaffold through a label-free detection system using inverse spectroscopic optical coherence tomography, which identifies changes to nanoscale tissue architecture associated with the presence of tumour cells. For patients at risk of recurrence, scaffold implantation following completion of primary therapy has the potential to identify metastatic disease at the earliest stage, enabling initiation of therapy while the disease burden is low.

Date: 2015
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DOI: 10.1038/ncomms9094

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